Overview

Dataset statistics

Number of variables32
Number of observations3334
Missing cells977
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory742.5 KiB
Average record size in memory228.0 B

Variable types

Numeric21
Categorical4
Text3
Boolean4

Alerts

Unnamed: 0 is highly overall correlated with osebuildingidHigh correlation
osebuildingid is highly overall correlated with Unnamed: 0High correlation
councildistrictcode is highly overall correlated with latitude and 1 other fieldsHigh correlation
propertygfatotal is highly overall correlated with propertygfabuilding_s and 4 other fieldsHigh correlation
propertygfabuilding_s is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
largestpropertyusetypegfa is highly overall correlated with propertygfatotal and 4 other fieldsHigh correlation
energystarscore is highly overall correlated with sourceeui_kbtu_sf and 2 other fieldsHigh correlation
siteeui_kbtu_sf is highly overall correlated with siteeuiwn_kbtu_sf and 6 other fieldsHigh correlation
siteeuiwn_kbtu_sf is highly overall correlated with siteeui_kbtu_sf and 5 other fieldsHigh correlation
sourceeui_kbtu_sf is highly overall correlated with energystarscore and 5 other fieldsHigh correlation
sourceeuiwn_kbtu_sf is highly overall correlated with energystarscore and 5 other fieldsHigh correlation
siteenergyuse_kbtu is highly overall correlated with propertygfatotal and 8 other fieldsHigh correlation
siteenergyusewn_kbtu is highly overall correlated with propertygfatotal and 8 other fieldsHigh correlation
totalghgemissions is highly overall correlated with propertygfatotal and 6 other fieldsHigh correlation
latitude is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
buildingtype is highly overall correlated with primarypropertytype and 1 other fieldsHigh correlation
primarypropertytype is highly overall correlated with buildingtype and 1 other fieldsHigh correlation
neighborhood is highly overall correlated with councildistrictcode and 1 other fieldsHigh correlation
defaultdata is highly overall correlated with buildingtype and 2 other fieldsHigh correlation
compliancestatus is highly overall correlated with energystarscore and 2 other fieldsHigh correlation
steamuse is highly imbalanced (76.4%)Imbalance
electricity is highly imbalanced (96.1%)Imbalance
defaultdata is highly imbalanced (78.9%)Imbalance
compliancestatus is highly imbalanced (99.2%)Imbalance
energystarscore has 825 (24.7%) missing valuesMissing
compliancestatus has 126 (3.8%) missing valuesMissing
numberofbuildings is highly skewed (γ1 = 43.70966104)Skewed
propertygfatotal is highly skewed (γ1 = 24.00490414)Skewed
propertygfabuilding_s is highly skewed (γ1 = 27.47908747)Skewed
largestpropertyusetypegfa is highly skewed (γ1 = 29.97068045)Skewed
siteenergyuse_kbtu is highly skewed (γ1 = 24.76248203)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
osebuildingid has unique valuesUnique
propertygfaparking has 2835 (85.0%) zerosZeros
sourceeuiwn_kbtu_sf has 36 (1.1%) zerosZeros

Reproduction

Analysis started2023-07-04 02:07:43.764592
Analysis finished2023-07-04 02:09:52.845386
Duration2 minutes and 9.08 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3334
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1684.1941
Minimum0
Maximum3375
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:53.015174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile166.65
Q1842.25
median1681.5
Q32529.75
95-th percentile3204.35
Maximum3375
Range3375
Interquartile range (IQR)1687.5

Descriptive statistics

Standard deviation974.70541
Coefficient of variation (CV)0.578737
Kurtosis-1.1991233
Mean1684.1941
Median Absolute Deviation (MAD)844.5
Skewness0.0032131493
Sum5615103
Variance950050.64
MonotonicityStrictly increasing
2023-07-04T04:09:53.407159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2104 1
 
< 0.1%
2239 1
 
< 0.1%
2240 1
 
< 0.1%
2241 1
 
< 0.1%
2242 1
 
< 0.1%
2243 1
 
< 0.1%
2244 1
 
< 0.1%
2245 1
 
< 0.1%
2246 1
 
< 0.1%
Other values (3324) 3324
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3375 1
< 0.1%
3374 1
< 0.1%
3373 1
< 0.1%
3372 1
< 0.1%
3371 1
< 0.1%
3370 1
< 0.1%
3369 1
< 0.1%
3368 1
< 0.1%
3367 1
< 0.1%
3366 1
< 0.1%

osebuildingid
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3334
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21170.645
Minimum1
Maximum50226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:53.810261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile271.25
Q119988.5
median23105.5
Q325988.5
95-th percentile49781.7
Maximum50226
Range50225
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation12221.494
Coefficient of variation (CV)0.5772849
Kurtosis0.64663913
Mean21170.645
Median Absolute Deviation (MAD)3013
Skewness-0.0098033577
Sum70582931
Variance1.4936491 × 108
MonotonicityNot monotonic
2023-07-04T04:09:54.159544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
24473 1
 
< 0.1%
24906 1
 
< 0.1%
24908 1
 
< 0.1%
24909 1
 
< 0.1%
24911 1
 
< 0.1%
24921 1
 
< 0.1%
24934 1
 
< 0.1%
24943 1
 
< 0.1%
24948 1
 
< 0.1%
Other values (3324) 3324
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
50226 1
< 0.1%
50225 1
< 0.1%
50224 1
< 0.1%
50223 1
< 0.1%
50222 1
< 0.1%
50221 1
< 0.1%
50220 1
< 0.1%
50219 1
< 0.1%
50212 1
< 0.1%
50210 1
< 0.1%

buildingtype
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
NonResidential
1442 
Multifamily LR (1-4)
999 
Multifamily MR (5-9)
578 
Multifamily HR (10+)
 
109
SPS-District K-12
 
97
Other values (3)
 
109

Length

Max length20
Median length20
Mean length17.165567
Min length6

Characters and Unicode

Total characters57230
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential

Common Values

ValueCountFrequency (%)
NonResidential 1442
43.3%
Multifamily LR (1-4) 999
30.0%
Multifamily MR (5-9) 578
17.3%
Multifamily HR (10+) 109
 
3.3%
SPS-District K-12 97
 
2.9%
Nonresidential COS 84
 
2.5%
Campus 24
 
0.7%
Nonresidential WA 1
 
< 0.1%

Length

2023-07-04T04:09:54.602382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-04T04:09:54.910231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1686
24.5%
nonresidential 1527
22.2%
lr 999
14.5%
1-4 999
14.5%
mr 578
 
8.4%
5-9 578
 
8.4%
hr 109
 
1.6%
10 109
 
1.6%
sps-district 97
 
1.4%
k-12 97
 
1.4%
Other values (3) 109
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6620
 
11.6%
l 4899
 
8.6%
3554
 
6.2%
t 3407
 
6.0%
a 3237
 
5.7%
R 3128
 
5.5%
n 3054
 
5.3%
e 3054
 
5.3%
M 2264
 
4.0%
- 1771
 
3.1%
Other values (30) 22242
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36068
63.0%
Uppercase Letter 8790
 
15.4%
Decimal Number 3566
 
6.2%
Space Separator 3554
 
6.2%
Dash Punctuation 1771
 
3.1%
Open Punctuation 1686
 
2.9%
Close Punctuation 1686
 
2.9%
Math Symbol 109
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6620
18.4%
l 4899
13.6%
t 3407
9.4%
a 3237
9.0%
n 3054
8.5%
e 3054
8.5%
u 1710
 
4.7%
m 1710
 
4.7%
f 1686
 
4.7%
y 1686
 
4.7%
Other values (6) 5005
13.9%
Uppercase Letter
ValueCountFrequency (%)
R 3128
35.6%
M 2264
25.8%
N 1527
17.4%
L 999
 
11.4%
S 278
 
3.2%
H 109
 
1.2%
C 108
 
1.2%
K 97
 
1.1%
P 97
 
1.1%
D 97
 
1.1%
Other values (3) 86
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1205
33.8%
4 999
28.0%
5 578
16.2%
9 578
16.2%
0 109
 
3.1%
2 97
 
2.7%
Space Separator
ValueCountFrequency (%)
3554
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1771
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1686
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1686
100.0%
Math Symbol
ValueCountFrequency (%)
+ 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44858
78.4%
Common 12372
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6620
14.8%
l 4899
10.9%
t 3407
 
7.6%
a 3237
 
7.2%
R 3128
 
7.0%
n 3054
 
6.8%
e 3054
 
6.8%
M 2264
 
5.0%
u 1710
 
3.8%
m 1710
 
3.8%
Other values (19) 11775
26.2%
Common
ValueCountFrequency (%)
3554
28.7%
- 1771
14.3%
( 1686
13.6%
) 1686
13.6%
1 1205
 
9.7%
4 999
 
8.1%
5 578
 
4.7%
9 578
 
4.7%
0 109
 
0.9%
+ 109
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6620
 
11.6%
l 4899
 
8.6%
3554
 
6.2%
t 3407
 
6.0%
a 3237
 
5.7%
R 3128
 
5.5%
n 3054
 
5.3%
e 3054
 
5.3%
M 2264
 
4.0%
- 1771
 
3.1%
Other values (30) 22242
38.9%

primarypropertytype
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
Low-Rise Multifamily
968 
Mid-Rise Multifamily
561 
Small- and Mid-Sized Office
288 
Other
253 
Warehouse
187 
Other values (19)
1077 

Length

Max length27
Median length22
Mean length17.181464
Min length5

Characters and Unicode

Total characters57283
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Low-Rise Multifamily 968
29.0%
Mid-Rise Multifamily 561
16.8%
Small- and Mid-Sized Office 288
 
8.6%
Other 253
 
7.6%
Warehouse 187
 
5.6%
Large Office 170
 
5.1%
K-12 School 137
 
4.1%
Mixed Use Property 132
 
4.0%
High-Rise Multifamily 104
 
3.1%
Retail Store 89
 
2.7%
Other values (14) 445
13.3%

Length

2023-07-04T04:09:55.168900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1633
23.6%
low-rise 968
14.0%
mid-rise 561
 
8.1%
office 500
 
7.2%
small 288
 
4.2%
and 288
 
4.2%
mid-sized 288
 
4.2%
other 253
 
3.7%
warehouse 199
 
2.9%
large 170
 
2.5%
Other values (28) 1777
25.7%

Most occurring characters

ValueCountFrequency (%)
i 7507
 
13.1%
e 4485
 
7.8%
l 4364
 
7.6%
3591
 
6.3%
a 3005
 
5.2%
t 2762
 
4.8%
f 2673
 
4.7%
M 2653
 
4.6%
- 2374
 
4.1%
s 2156
 
3.8%
Other values (33) 21713
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42458
74.1%
Uppercase Letter 8546
 
14.9%
Space Separator 3591
 
6.3%
Dash Punctuation 2374
 
4.1%
Decimal Number 274
 
0.5%
Other Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7507
17.7%
e 4485
10.6%
l 4364
10.3%
a 3005
 
7.1%
t 2762
 
6.5%
f 2673
 
6.3%
s 2156
 
5.1%
o 2088
 
4.9%
m 2051
 
4.8%
u 1982
 
4.7%
Other values (14) 9385
22.1%
Uppercase Letter
ValueCountFrequency (%)
M 2653
31.0%
R 1769
20.7%
L 1148
13.4%
S 983
 
11.5%
O 753
 
8.8%
W 268
 
3.1%
H 213
 
2.5%
U 157
 
1.8%
C 143
 
1.7%
K 137
 
1.6%
Other values (4) 322
 
3.8%
Decimal Number
ValueCountFrequency (%)
1 137
50.0%
2 137
50.0%
Space Separator
ValueCountFrequency (%)
3591
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2374
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51004
89.0%
Common 6279
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7507
14.7%
e 4485
 
8.8%
l 4364
 
8.6%
a 3005
 
5.9%
t 2762
 
5.4%
f 2673
 
5.2%
M 2653
 
5.2%
s 2156
 
4.2%
o 2088
 
4.1%
m 2051
 
4.0%
Other values (28) 17260
33.8%
Common
ValueCountFrequency (%)
3591
57.2%
- 2374
37.8%
1 137
 
2.2%
2 137
 
2.2%
/ 40
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7507
 
13.1%
e 4485
 
7.8%
l 4364
 
7.6%
3591
 
6.3%
a 3005
 
5.2%
t 2762
 
4.8%
f 2673
 
4.7%
M 2653
 
4.6%
- 2374
 
4.1%
s 2156
 
3.8%
Other values (33) 21713
37.9%
Distinct3228
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:55.524750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length10
Mean length10.005099
Min length9

Characters and Unicode

Total characters33357
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3152 ?
Unique (%)94.5%

Sample

1st row0659000030
2nd row0659000220
3rd row0659000475
4th row0659000640
5th row0659000970
ValueCountFrequency (%)
1625049001 8
 
0.2%
3224049012 5
 
0.1%
0925049346 5
 
0.1%
0002400002 5
 
0.1%
7666203240 4
 
0.1%
3624039009 4
 
0.1%
8809700040 3
 
0.1%
1985200003 3
 
0.1%
5036300605 3
 
0.1%
8632880000 3
 
0.1%
Other values (3219) 3293
98.7%
2023-07-04T04:09:56.290781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11207
33.6%
2 3127
 
9.4%
5 2906
 
8.7%
6 2683
 
8.0%
1 2662
 
8.0%
9 2359
 
7.1%
7 2336
 
7.0%
4 2143
 
6.4%
3 2041
 
6.1%
8 1886
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33350
> 99.9%
Lowercase Letter 3
 
< 0.1%
Space Separator 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11207
33.6%
2 3127
 
9.4%
5 2906
 
8.7%
6 2683
 
8.0%
1 2662
 
8.0%
9 2359
 
7.1%
7 2336
 
7.0%
4 2143
 
6.4%
3 2041
 
6.1%
8 1886
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33354
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11207
33.6%
2 3127
 
9.4%
5 2906
 
8.7%
6 2683
 
8.0%
1 2662
 
8.0%
9 2359
 
7.1%
7 2336
 
7.0%
4 2143
 
6.4%
3 2041
 
6.1%
8 1886
 
5.7%
Other values (2) 4
 
< 0.1%
Latin
ValueCountFrequency (%)
a 1
33.3%
n 1
33.3%
d 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11207
33.6%
2 3127
 
9.4%
5 2906
 
8.7%
6 2683
 
8.0%
1 2662
 
8.0%
9 2359
 
7.1%
7 2336
 
7.0%
4 2143
 
6.4%
3 2041
 
6.1%
8 1886
 
5.7%
Other values (5) 7
 
< 0.1%

councildistrictcode
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4463107
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:56.594692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1177383
Coefficient of variation (CV)0.47629111
Kurtosis-1.4439402
Mean4.4463107
Median Absolute Deviation (MAD)2
Skewness-0.073983963
Sum14824
Variance4.4848154
MonotonicityNot monotonic
2023-07-04T04:09:56.920670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1025
30.7%
3 588
17.6%
2 503
15.1%
4 361
 
10.8%
5 337
 
10.1%
1 274
 
8.2%
6 246
 
7.4%
ValueCountFrequency (%)
1 274
 
8.2%
2 503
15.1%
3 588
17.6%
4 361
 
10.8%
5 337
 
10.1%
6 246
 
7.4%
7 1025
30.7%
ValueCountFrequency (%)
7 1025
30.7%
6 246
 
7.4%
5 337
 
10.1%
4 361
 
10.8%
3 588
17.6%
2 503
15.1%
1 274
 
8.2%

neighborhood
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
DOWNTOWN
564 
EAST
448 
MAGNOLIA / QUEEN ANNE
418 
GREATER DUWAMISH
371 
NORTHEAST
274 
Other values (8)
1259 

Length

Max length21
Median length10
Mean length10.110678
Min length4

Characters and Unicode

Total characters33709
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN 564
16.9%
EAST 448
13.4%
MAGNOLIA / QUEEN ANNE 418
12.5%
GREATER DUWAMISH 371
11.1%
NORTHEAST 274
8.2%
LAKE UNION 251
7.5%
NORTHWEST 220
 
6.6%
NORTH 186
 
5.6%
SOUTHWEST 158
 
4.7%
BALLARD 133
 
4.0%
Other values (3) 311
9.3%

Length

2023-07-04T04:09:57.501602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 564
10.8%
east 448
 
8.6%
magnolia 418
 
8.0%
418
 
8.0%
queen 418
 
8.0%
anne 418
 
8.0%
greater 371
 
7.1%
duwamish 371
 
7.1%
northeast 274
 
5.3%
union 251
 
4.8%
Other values (8) 1259
24.2%

Most occurring characters

ValueCountFrequency (%)
N 4113
12.2%
E 3743
11.1%
A 3461
10.3%
T 3194
9.5%
O 2730
 
8.1%
W 1877
 
5.6%
1876
 
5.6%
S 1819
 
5.4%
R 1771
 
5.3%
H 1304
 
3.9%
Other values (11) 7821
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31415
93.2%
Space Separator 1876
 
5.6%
Other Punctuation 418
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4113
13.1%
E 3743
11.9%
A 3461
11.0%
T 3194
10.2%
O 2730
8.7%
W 1877
 
6.0%
S 1819
 
5.8%
R 1771
 
5.6%
H 1304
 
4.2%
U 1293
 
4.1%
Other values (9) 6110
19.4%
Space Separator
ValueCountFrequency (%)
1876
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31415
93.2%
Common 2294
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4113
13.1%
E 3743
11.9%
A 3461
11.0%
T 3194
10.2%
O 2730
8.7%
W 1877
 
6.0%
S 1819
 
5.8%
R 1771
 
5.6%
H 1304
 
4.2%
U 1293
 
4.1%
Other values (9) 6110
19.4%
Common
ValueCountFrequency (%)
1876
81.8%
/ 418
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4113
12.2%
E 3743
11.1%
A 3461
10.3%
T 3194
9.5%
O 2730
 
8.1%
W 1877
 
5.6%
1876
 
5.6%
S 1819
 
5.4%
R 1771
 
5.3%
H 1304
 
3.9%
Other values (11) 7821
23.2%

numberofbuildings
Real number (ℝ)

SKEWED 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1340732
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:58.099152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1098734
Coefficient of variation (CV)1.8604385
Kurtosis2219.3838
Mean1.1340732
Median Absolute Deviation (MAD)0
Skewness43.709661
Sum3781
Variance4.4515659
MonotonicityNot monotonic
2023-07-04T04:09:58.599130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 3235
97.0%
2 36
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.1%
8 3
 
0.1%
14 2
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
Other values (6) 6
 
0.2%
ValueCountFrequency (%)
1 3235
97.0%
2 36
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 9
 
0.3%
6 5
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
111 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
0.1%
11 1
 
< 0.1%
10 2
0.1%
9 2
0.1%
8 3
0.1%
7 1
 
< 0.1%

numberoffloors
Real number (ℝ)

Distinct49
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7228554
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:09:59.081840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.5107542
Coefficient of variation (CV)1.1668268
Kurtosis55.885782
Mean4.7228554
Median Absolute Deviation (MAD)2
Skewness5.9257374
Sum15746
Variance30.368412
MonotonicityNot monotonic
2023-07-04T04:09:59.529171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3 681
20.4%
4 678
20.3%
1 478
14.3%
2 433
13.0%
6 304
9.1%
5 294
8.8%
7 146
 
4.4%
8 63
 
1.9%
10 32
 
1.0%
11 32
 
1.0%
Other values (39) 193
 
5.8%
ValueCountFrequency (%)
1 478
14.3%
2 433
13.0%
3 681
20.4%
4 678
20.3%
5 294
8.8%
6 304
9.1%
7 146
 
4.4%
8 63
 
1.9%
9 18
 
0.5%
10 32
 
1.0%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 3
0.1%

propertygfatotal
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3159
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95408.128
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:00.221982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21301.95
Q128534.75
median44454
Q391550
95-th percentile320811.45
Maximum9320156
Range9308871
Interquartile range (IQR)63015.25

Descriptive statistics

Standard deviation220109.49
Coefficient of variation (CV)2.3070307
Kurtosis935.94605
Mean95408.128
Median Absolute Deviation (MAD)20003.5
Skewness24.004904
Sum3.180907 × 108
Variance4.8448185 × 1010
MonotonicityNot monotonic
2023-07-04T04:10:00.765968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
22320 4
 
0.1%
30240 4
 
0.1%
30720 4
 
0.1%
20000 3
 
0.1%
43380 3
 
0.1%
Other values (3149) 3279
98.4%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
13661 1
< 0.1%
14101 1
< 0.1%
15398 1
< 0.1%
16000 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 2
0.1%
1380959 1
< 0.1%

propertygfaparking
Real number (ℝ)

ZEROS 

Distinct491
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8072.251
Minimum0
Maximum512608
Zeros2835
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:01.384090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47839.05
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32511.558
Coefficient of variation (CV)4.0275702
Kurtosis58.284078
Mean8072.251
Median Absolute Deviation (MAD)0
Skewness6.6140599
Sum26912885
Variance1.0570014 × 109
MonotonicityNot monotonic
2023-07-04T04:10:02.001925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2835
85.0%
13320 3
 
0.1%
12960 2
 
0.1%
10800 2
 
0.1%
100176 2
 
0.1%
22000 2
 
0.1%
30000 2
 
0.1%
25800 2
 
0.1%
20416 2
 
0.1%
3029 1
 
< 0.1%
Other values (481) 481
 
14.4%
ValueCountFrequency (%)
0 2835
85.0%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

propertygfabuilding_s
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3157
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87335.877
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:02.549771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21038.6
Q127794
median43355
Q384698.5
95-th percentile283450.4
Maximum9320156
Range9316520
Interquartile range (IQR)56904.5

Descriptive statistics

Standard deviation209159.59
Coefficient of variation (CV)2.3948874
Kurtosis1148.5293
Mean87335.877
Median Absolute Deviation (MAD)19067
Skewness27.479087
Sum2.9117782 × 108
Variance4.3747733 × 1010
MonotonicityNot monotonic
2023-07-04T04:10:02.966474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
30720 4
 
0.1%
22320 4
 
0.1%
24288 3
 
0.1%
25380 3
 
0.1%
Other values (3147) 3279
98.4%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%
Distinct463
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:03.548495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length255
Median length162
Mean length25.959808
Min length5

Characters and Unicode

Total characters86550
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique311 ?
Unique (%)9.3%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
multifamily 1691
17.2%
housing 1691
17.2%
parking 1079
11.0%
office 951
 
9.7%
store 467
 
4.8%
other 415
 
4.2%
retail 399
 
4.1%
warehouse 277
 
2.8%
non-refrigerated 260
 
2.7%
180
 
1.8%
Other values (97) 2401
24.5%
2023-07-04T04:10:04.724307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9282
 
10.7%
6477
 
7.5%
e 5641
 
6.5%
a 5202
 
6.0%
l 4908
 
5.7%
t 4898
 
5.7%
u 4223
 
4.9%
r 4215
 
4.9%
n 4142
 
4.8%
o 3977
 
4.6%
Other values (42) 33585
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65782
76.0%
Uppercase Letter 10264
 
11.9%
Space Separator 6477
 
7.5%
Other Punctuation 3032
 
3.5%
Dash Punctuation 629
 
0.7%
Decimal Number 290
 
0.3%
Close Punctuation 38
 
< 0.1%
Open Punctuation 38
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9282
14.1%
e 5641
 
8.6%
a 5202
 
7.9%
l 4908
 
7.5%
t 4898
 
7.4%
u 4223
 
6.4%
r 4215
 
6.4%
n 4142
 
6.3%
o 3977
 
6.0%
f 3936
 
6.0%
Other values (12) 15358
23.3%
Uppercase Letter
ValueCountFrequency (%)
H 1891
18.4%
M 1840
17.9%
O 1388
13.5%
P 1227
12.0%
S 1022
10.0%
R 954
9.3%
W 353
 
3.4%
C 340
 
3.3%
N 266
 
2.6%
F 221
 
2.2%
Other values (11) 762
7.4%
Other Punctuation
ValueCountFrequency (%)
, 2659
87.7%
/ 361
 
11.9%
& 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 145
50.0%
1 145
50.0%
Space Separator
ValueCountFrequency (%)
6477
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 629
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76046
87.9%
Common 10504
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9282
 
12.2%
e 5641
 
7.4%
a 5202
 
6.8%
l 4908
 
6.5%
t 4898
 
6.4%
u 4223
 
5.6%
r 4215
 
5.5%
n 4142
 
5.4%
o 3977
 
5.2%
f 3936
 
5.2%
Other values (33) 25622
33.7%
Common
ValueCountFrequency (%)
6477
61.7%
, 2659
25.3%
- 629
 
6.0%
/ 361
 
3.4%
2 145
 
1.4%
1 145
 
1.4%
) 38
 
0.4%
( 38
 
0.4%
& 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9282
 
10.7%
6477
 
7.5%
e 5641
 
6.5%
a 5202
 
6.0%
l 4908
 
5.7%
t 4898
 
5.7%
u 4223
 
4.9%
r 4215
 
4.9%
n 4142
 
4.8%
o 3977
 
4.6%
Other values (42) 33585
38.8%
Distinct56
Distinct (%)1.7%
Missing11
Missing (%)0.3%
Memory size26.2 KiB
2023-07-04T04:10:05.334367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.28378
Min length5

Characters and Unicode

Total characters54111
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1651
27.1%
housing 1651
27.1%
office 536
 
8.8%
warehouse 211
 
3.5%
non-refrigerated 199
 
3.3%
other 176
 
2.9%
store 138
 
2.3%
k-12 137
 
2.2%
school 137
 
2.2%
facility 98
 
1.6%
Other values (79) 1163
19.1%
2023-07-04T04:10:06.381731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6762
 
12.5%
l 4091
 
7.6%
u 3762
 
7.0%
t 3069
 
5.7%
o 3046
 
5.6%
e 3016
 
5.6%
f 2975
 
5.5%
a 2780
 
5.1%
2774
 
5.1%
n 2375
 
4.4%
Other values (41) 19461
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43994
81.3%
Uppercase Letter 6396
 
11.8%
Space Separator 2774
 
5.1%
Dash Punctuation 443
 
0.8%
Decimal Number 274
 
0.5%
Other Punctuation 196
 
0.4%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6762
15.4%
l 4091
9.3%
u 3762
 
8.6%
t 3069
 
7.0%
o 3046
 
6.9%
e 3016
 
6.9%
f 2975
 
6.8%
a 2780
 
6.3%
n 2375
 
5.4%
s 2172
 
4.9%
Other values (11) 9946
22.6%
Uppercase Letter
ValueCountFrequency (%)
H 1781
27.8%
M 1736
27.1%
O 724
11.3%
S 472
 
7.4%
R 390
 
6.1%
W 281
 
4.4%
C 200
 
3.1%
N 199
 
3.1%
K 137
 
2.1%
F 109
 
1.7%
Other values (11) 367
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 166
84.7%
, 20
 
10.2%
& 10
 
5.1%
Decimal Number
ValueCountFrequency (%)
2 137
50.0%
1 137
50.0%
Space Separator
ValueCountFrequency (%)
2774
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 443
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50390
93.1%
Common 3721
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6762
13.4%
l 4091
 
8.1%
u 3762
 
7.5%
t 3069
 
6.1%
o 3046
 
6.0%
e 3016
 
6.0%
f 2975
 
5.9%
a 2780
 
5.5%
n 2375
 
4.7%
s 2172
 
4.3%
Other values (32) 16342
32.4%
Common
ValueCountFrequency (%)
2774
74.5%
- 443
 
11.9%
/ 166
 
4.5%
2 137
 
3.7%
1 137
 
3.7%
, 20
 
0.5%
( 17
 
0.5%
) 17
 
0.5%
& 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6762
 
12.5%
l 4091
 
7.6%
u 3762
 
7.0%
t 3069
 
5.7%
o 3046
 
5.6%
e 3016
 
5.6%
f 2975
 
5.5%
a 2780
 
5.1%
2774
 
5.1%
n 2375
 
4.4%
Other values (41) 19461
36.0%

largestpropertyusetypegfa
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3096
Distinct (%)93.2%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean79499.113
Minimum5656
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:06.878098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17528.6
Q125148.5
median39960
Q376902.5
95-th percentile244975.2
Maximum9320156
Range9314500
Interquartile range (IQR)51754

Descriptive statistics

Standard deviation202640.47
Coefficient of variation (CV)2.5489652
Kurtosis1309.0358
Mean79499.113
Median Absolute Deviation (MAD)17619
Skewness29.97068
Sum2.6417555 × 108
Variance4.1063162 × 1010
MonotonicityNot monotonic
2023-07-04T04:10:07.446408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 9
 
0.3%
22000 8
 
0.2%
30000 8
 
0.2%
21600 7
 
0.2%
20000 7
 
0.2%
15000 5
 
0.1%
45000 5
 
0.1%
24288 5
 
0.1%
36000 5
 
0.1%
28800 5
 
0.1%
Other values (3086) 3259
97.8%
(Missing) 11
 
0.3%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
7387 1
< 0.1%
7501 1
< 0.1%
7583 1
< 0.1%
7758 1
< 0.1%
8061 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
1350182 1
< 0.1%
1314475 1
< 0.1%
1191115 1
< 0.1%
1172127 1
< 0.1%
1072000 1
< 0.1%

energystarscore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)4.0%
Missing825
Missing (%)24.7%
Infinite0
Infinite (%)0.0%
Mean67.81666
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:07.844602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.705492
Coefficient of variation (CV)0.39378954
Kurtosis-0.22156724
Mean67.81666
Median Absolute Deviation (MAD)17
Skewness-0.85580171
Sum170152
Variance713.18328
MonotonicityNot monotonic
2023-07-04T04:10:08.200219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 92
 
2.8%
98 72
 
2.2%
96 64
 
1.9%
89 58
 
1.7%
93 57
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
99 48
 
1.4%
Other values (90) 1916
57.5%
(Missing) 825
24.7%
ValueCountFrequency (%)
1 33
1.0%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.1%
5 8
 
0.2%
6 8
 
0.2%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.1%
10 10
 
0.3%
ValueCountFrequency (%)
100 92
2.8%
99 48
1.4%
98 72
2.2%
97 48
1.4%
96 64
1.9%
95 51
1.5%
94 49
1.5%
93 57
1.7%
92 53
1.6%
91 49
1.5%

siteeui_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1066
Distinct (%)32.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean54.763475
Minimum0
Maximum834.40002
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:08.633404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.4
Q128.1
median38.799999
Q360.400002
95-th percentile144.5
Maximum834.40002
Range834.40002
Interquartile range (IQR)32.300001

Descriptive statistics

Standard deviation55.938665
Coefficient of variation (CV)1.0214594
Kurtosis41.236394
Mean54.763475
Median Absolute Deviation (MAD)13.400002
Skewness5.0651096
Sum182471.9
Variance3129.1343
MonotonicityNot monotonic
2023-07-04T04:10:09.028115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.70000076 17
 
0.5%
28.79999924 17
 
0.5%
24.20000076 16
 
0.5%
0 16
 
0.5%
32 15
 
0.4%
28.89999962 14
 
0.4%
31.70000076 14
 
0.4%
26.39999962 14
 
0.4%
26.60000038 13
 
0.4%
22.79999924 13
 
0.4%
Other values (1056) 3183
95.5%
ValueCountFrequency (%)
0 16
0.5%
1.399999976 1
 
< 0.1%
2.099999905 1
 
< 0.1%
2.299999952 1
 
< 0.1%
3 1
 
< 0.1%
3.200000048 1
 
< 0.1%
3.5 2
 
0.1%
3.599999905 2
 
0.1%
3.799999952 1
 
< 0.1%
4.300000191 1
 
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
696.7000122 1
< 0.1%
694.7000122 1
< 0.1%
639.7000122 1
< 0.1%
593.5999756 1
< 0.1%
465.5 1
< 0.1%
456.6000061 1
< 0.1%
438.2000122 1
< 0.1%
412.7000122 1
< 0.1%

siteeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1085
Distinct (%)32.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean57.073987
Minimum0
Maximum834.40002
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:09.375592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.700001
Q129.5
median41
Q364.300003
95-th percentile147.7
Maximum834.40002
Range834.40002
Interquartile range (IQR)34.800003

Descriptive statistics

Standard deviation56.819416
Coefficient of variation (CV)0.99553963
Kurtosis38.822409
Mean57.073987
Median Absolute Deviation (MAD)14.200001
Skewness4.9093207
Sum190227.6
Variance3228.446
MonotonicityNot monotonic
2023-07-04T04:10:09.729929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
29.5 17
 
0.5%
30.79999924 15
 
0.4%
29 14
 
0.4%
31.60000038 14
 
0.4%
27.89999962 14
 
0.4%
32.20000076 14
 
0.4%
30.20000076 14
 
0.4%
33.59999847 13
 
0.4%
28.10000038 13
 
0.4%
Other values (1075) 3176
95.3%
ValueCountFrequency (%)
0 29
0.9%
1.5 1
 
< 0.1%
2.099999905 1
 
< 0.1%
2.299999952 1
 
< 0.1%
3 1
 
< 0.1%
3.200000048 1
 
< 0.1%
3.5 1
 
< 0.1%
3.599999905 2
 
0.1%
4 1
 
< 0.1%
4.300000191 2
 
0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
694.7000122 1
< 0.1%
693.0999756 1
< 0.1%
639.7999878 1
< 0.1%
593.5999756 1
< 0.1%
468.7000122 1
< 0.1%
467 1
< 0.1%
460.1000061 1
< 0.1%
426.6000061 1
< 0.1%

sourceeui_kbtu_sf
Real number (ℝ)

HIGH CORRELATION 

Distinct1623
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.22463
Minimum0
Maximum2620
Zeros24
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:10.107842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.494999
Q175
median96.400002
Q3143.875
95-th percentile349.455
Maximum2620
Range2620
Interquartile range (IQR)68.874996

Descriptive statistics

Standard deviation137.78693
Coefficient of variation (CV)1.0265399
Kurtosis81.265223
Mean134.22463
Median Absolute Deviation (MAD)27.5
Skewness6.7414881
Sum447504.9
Variance18985.239
MonotonicityNot monotonic
2023-07-04T04:10:10.541203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
0.7%
83.69999695 9
 
0.3%
68.09999847 9
 
0.3%
73.09999847 8
 
0.2%
78.59999847 8
 
0.2%
69.69999695 8
 
0.2%
90.5 8
 
0.2%
95 8
 
0.2%
94.09999847 8
 
0.2%
87.69999695 8
 
0.2%
Other values (1613) 3236
97.1%
ValueCountFrequency (%)
0 24
0.7%
4.5 1
 
< 0.1%
6.599999905 2
 
0.1%
6.900000095 1
 
< 0.1%
9 1
 
< 0.1%
9.5 1
 
< 0.1%
9.899999619 1
 
< 0.1%
10.19999981 1
 
< 0.1%
11.10000038 1
 
< 0.1%
11.19999981 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2007.900024 1
< 0.1%
1527.300049 1
< 0.1%
1206.699951 1
< 0.1%
1150.300049 1
< 0.1%
1026.599976 1
< 0.1%
962.0999756 1
< 0.1%
912.7999878 1
< 0.1%

sourceeuiwn_kbtu_sf
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1669
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.78359
Minimum0
Maximum2620
Zeros36
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:10.933083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.665
Q178.724998
median101.3
Q3148.3
95-th percentile350.285
Maximum2620
Range2620
Interquartile range (IQR)69.575005

Descriptive statistics

Standard deviation137.55422
Coefficient of variation (CV)0.99833529
Kurtosis81.154874
Mean137.78359
Median Absolute Deviation (MAD)28.300003
Skewness6.7209135
Sum459370.5
Variance18921.165
MonotonicityNot monotonic
2023-07-04T04:10:11.355077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
1.1%
73.59999847 9
 
0.3%
87.30000305 9
 
0.3%
75.5 8
 
0.2%
98.90000153 8
 
0.2%
83.5 8
 
0.2%
102.4000015 8
 
0.2%
93.59999847 8
 
0.2%
104.5999985 8
 
0.2%
84.90000153 8
 
0.2%
Other values (1659) 3224
96.7%
ValueCountFrequency (%)
0 36
1.1%
4.599999905 1
 
< 0.1%
6.599999905 1
 
< 0.1%
6.900000095 1
 
< 0.1%
7.400000095 1
 
< 0.1%
9 1
 
< 0.1%
9.5 1
 
< 0.1%
10 1
 
< 0.1%
10.30000019 1
 
< 0.1%
11.19999981 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2008 1
< 0.1%
1527.300049 1
< 0.1%
1195.099976 1
< 0.1%
1138.400024 1
< 0.1%
1001 1
< 0.1%
954 1
< 0.1%
919.2999878 1
< 0.1%

siteenergyuse_kbtu
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3317
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5421880.3
Minimum0
Maximum8.7392371 × 108
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:11.722434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile507617.73
Q1936592.41
median1812395.8
Q34223663.9
95-th percentile18143745
Maximum8.7392371 × 108
Range8.7392371 × 108
Interquartile range (IQR)3287071.5

Descriptive statistics

Standard deviation21712336
Coefficient of variation (CV)4.0045767
Kurtosis851.90528
Mean5421880.3
Median Absolute Deviation (MAD)1071607.6
Skewness24.762482
Sum1.8076549 × 1010
Variance4.7142552 × 1014
MonotonicityNot monotonic
2023-07-04T04:10:12.149357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.5%
7226362.5 1
 
< 0.1%
958242.875 1
 
< 0.1%
1206165.75 1
 
< 0.1%
1302192.875 1
 
< 0.1%
150167.7969 1
 
< 0.1%
1386445.375 1
 
< 0.1%
1331469.75 1
 
< 0.1%
421389.4063 1
 
< 0.1%
12213423 1
 
< 0.1%
Other values (3307) 3307
99.2%
ValueCountFrequency (%)
0 18
0.5%
57133.19922 1
 
< 0.1%
79711.79688 1
 
< 0.1%
90558.70313 1
 
< 0.1%
97690.39844 1
 
< 0.1%
106918 1
 
< 0.1%
111969.7031 1
 
< 0.1%
113130 1
 
< 0.1%
116486.6016 1
 
< 0.1%
117438.3984 1
 
< 0.1%
ValueCountFrequency (%)
873923712 1
< 0.1%
448385312 1
< 0.1%
293090784 1
< 0.1%
291614432 1
< 0.1%
274682208 1
< 0.1%
253832464 1
< 0.1%
163945984 1
< 0.1%
143423024 1
< 0.1%
131373880 1
< 0.1%
114648520 1
< 0.1%

siteenergyusewn_kbtu
Real number (ℝ)

HIGH CORRELATION 

Distinct3304
Distinct (%)99.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5292551.5
Minimum0
Maximum4.7161386 × 108
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:12.529980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile527651.54
Q1986329.5
median1916863.4
Q34381679
95-th percentile18192559
Maximum4.7161386 × 108
Range4.7161386 × 108
Interquartile range (IQR)3395349.5

Descriptive statistics

Standard deviation16002488
Coefficient of variation (CV)3.0235867
Kurtosis332.78844
Mean5292551.5
Median Absolute Deviation (MAD)1128377.7
Skewness15.247975
Sum1.7640074 × 1010
Variance2.5607963 × 1014
MonotonicityNot monotonic
2023-07-04T04:10:12.952500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
2127889.25 2
 
0.1%
6739209 1
 
< 0.1%
1024822.188 1
 
< 0.1%
1342448.875 1
 
< 0.1%
909471.875 1
 
< 0.1%
509741.1875 1
 
< 0.1%
1355995.25 1
 
< 0.1%
1439042.25 1
 
< 0.1%
150167.7969 1
 
< 0.1%
Other values (3294) 3294
98.8%
ValueCountFrequency (%)
0 29
0.9%
58114.19922 1
 
< 0.1%
79967.89844 1
 
< 0.1%
90558.70313 1
 
< 0.1%
98862.89844 1
 
< 0.1%
109471.7969 1
 
< 0.1%
116486.6016 1
 
< 0.1%
116642.5 1
 
< 0.1%
120610.5 1
 
< 0.1%
127374 1
 
< 0.1%
ValueCountFrequency (%)
471613856 1
< 0.1%
296671744 1
< 0.1%
295929888 1
< 0.1%
274725984 1
< 0.1%
257764208 1
< 0.1%
167207104 1
< 0.1%
147299056 1
< 0.1%
137106112 1
< 0.1%
123205560 1
< 0.1%
103985264 1
< 0.1%

steamuse
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3205 
True
 
129
ValueCountFrequency (%)
False 3205
96.1%
True 129
 
3.9%
2023-07-04T04:10:13.465115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

electricity
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
3320 
False
 
14
ValueCountFrequency (%)
True 3320
99.6%
False 14
 
0.4%
2023-07-04T04:10:13.800828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

naturalgas
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
True
2094 
False
1240 
ValueCountFrequency (%)
True 2094
62.8%
False 1240
37.2%
2023-07-04T04:10:14.086905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

defaultdata
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3223 
True
 
111
ValueCountFrequency (%)
False 3223
96.7%
True 111
 
3.3%
2023-07-04T04:10:14.514224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

compliancestatus
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing126
Missing (%)3.8%
Memory size26.2 KiB
Compliant
3206 
Non-Compliant
 
2

Length

Max length13
Median length9
Mean length9.0024938
Min length9

Characters and Unicode

Total characters28880
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowCompliant
3rd rowCompliant
4th rowCompliant
5th rowCompliant

Common Values

ValueCountFrequency (%)
Compliant 3206
96.2%
Non-Compliant 2
 
0.1%
(Missing) 126
 
3.8%

Length

2023-07-04T04:10:15.002552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-04T04:10:15.264593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 3206
99.9%
non-compliant 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 3210
11.1%
n 3210
11.1%
C 3208
11.1%
m 3208
11.1%
p 3208
11.1%
l 3208
11.1%
i 3208
11.1%
a 3208
11.1%
t 3208
11.1%
N 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25668
88.9%
Uppercase Letter 3210
 
11.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3210
12.5%
n 3210
12.5%
m 3208
12.5%
p 3208
12.5%
l 3208
12.5%
i 3208
12.5%
a 3208
12.5%
t 3208
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 3208
99.9%
N 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28878
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3210
11.1%
n 3210
11.1%
C 3208
11.1%
m 3208
11.1%
p 3208
11.1%
l 3208
11.1%
i 3208
11.1%
a 3208
11.1%
t 3208
11.1%
N 2
 
< 0.1%
Common
ValueCountFrequency (%)
- 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3210
11.1%
n 3210
11.1%
C 3208
11.1%
m 3208
11.1%
p 3208
11.1%
l 3208
11.1%
i 3208
11.1%
a 3208
11.1%
t 3208
11.1%
N 2
 
< 0.1%

totalghgemissions
Real number (ℝ)

HIGH CORRELATION 

Distinct2790
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.14511
Minimum0
Maximum16870.98
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:15.604302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.9
Q19.6625
median34.125
Q394.0175
95-th percentile392.4785
Maximum16870.98
Range16870.98
Interquartile range (IQR)84.355

Descriptive statistics

Standard deviation541.24136
Coefficient of variation (CV)4.5048972
Kurtosis471.03548
Mean120.14511
Median Absolute Deviation (MAD)28.04
Skewness19.410543
Sum400563.79
Variance292942.21
MonotonicityNot monotonic
2023-07-04T04:10:16.016930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.3%
3.95 7
 
0.2%
4.2 6
 
0.2%
6.18 5
 
0.1%
3.54 5
 
0.1%
4.52 5
 
0.1%
4.43 5
 
0.1%
4.8 5
 
0.1%
5.46 5
 
0.1%
9.29 5
 
0.1%
Other values (2780) 3277
98.3%
ValueCountFrequency (%)
0 9
0.3%
0.4 1
 
< 0.1%
0.63 1
 
< 0.1%
0.68 1
 
< 0.1%
0.75 1
 
< 0.1%
0.79 1
 
< 0.1%
0.81 1
 
< 0.1%
0.82 1
 
< 0.1%
0.86 1
 
< 0.1%
0.87 1
 
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
11140.56 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%

zipcode
Real number (ℝ)

Distinct60
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98117.005
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:16.425848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.659486
Coefficient of variation (CV)0.00019017586
Kurtosis10.444809
Mean98117.005
Median Absolute Deviation (MAD)10
Skewness1.994031
Sum3.2712209 × 108
Variance348.17641
MonotonicityNot monotonic
2023-07-04T04:10:16.867411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 292
 
8.8%
98104 246
 
7.4%
98122 239
 
7.2%
98101 226
 
6.8%
98105 186
 
5.6%
98121 185
 
5.5%
98134 184
 
5.5%
98102 167
 
5.0%
98119 165
 
4.9%
98103 160
 
4.8%
Other values (50) 1284
38.5%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
98028 1
< 0.1%
98033 1
< 0.1%
98040 1
< 0.1%
98053 1
< 0.1%
98070 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 70
2.1%
98198 1
 
< 0.1%
98195 10
 
0.3%
98191 1
 
< 0.1%
98185 1
 
< 0.1%
98181 1
 
< 0.1%
98178 4
 
0.1%
98177 2
 
0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2848
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.624195
Minimum47.49917
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:17.254853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.49917
5-th percentile47.541602
Q147.60012
median47.618835
Q347.657232
95-th percentile47.713091
Maximum47.73387
Range0.2347
Interquartile range (IQR)0.0571125

Descriptive statistics

Standard deviation0.047823298
Coefficient of variation (CV)0.0010041807
Kurtosis-0.14467741
Mean47.624195
Median Absolute Deviation (MAD)0.028415
Skewness0.13760485
Sum158779.07
Variance0.0022870678
MonotonicityNot monotonic
2023-07-04T04:10:17.688709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246 9
 
0.3%
47.61598 7
 
0.2%
47.62208 6
 
0.2%
47.62395 5
 
0.1%
47.52549 5
 
0.1%
47.61543 5
 
0.1%
47.6239 4
 
0.1%
47.52254 4
 
0.1%
47.5829 4
 
0.1%
47.61048 4
 
0.1%
Other values (2838) 3281
98.4%
ValueCountFrequency (%)
47.49917 1
< 0.1%
47.50061895 1
< 0.1%
47.50224 1
< 0.1%
47.50959 1
< 0.1%
47.5097 1
< 0.1%
47.51018 1
< 0.1%
47.51042 1
< 0.1%
47.51098 1
< 0.1%
47.51104 1
< 0.1%
47.51127 2
0.1%
ValueCountFrequency (%)
47.73387 1
< 0.1%
47.73375 1
< 0.1%
47.73368 1
< 0.1%
47.7336 1
< 0.1%
47.73357 1
< 0.1%
47.73351 1
< 0.1%
47.73331 1
< 0.1%
47.73316 1
< 0.1%
47.73315 1
< 0.1%
47.73279 1
< 0.1%

longitude
Real number (ℝ)

Distinct2632
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33475
Minimum-122.41425
Maximum-122.22097
Zeros0
Zeros (%)0.0%
Negative3334
Negative (%)100.0%
Memory size26.2 KiB
2023-07-04T04:10:18.230001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.38651
Q1-122.35053
median-122.33248
Q3-122.31943
95-th percentile-122.28981
Maximum-122.22097
Range0.1932841
Interquartile range (IQR)0.031095

Descriptive statistics

Standard deviation0.027164202
Coefficient of variation (CV)-0.00022204813
Kurtosis0.26755314
Mean-122.33475
Median Absolute Deviation (MAD)0.015075
Skewness-0.13665451
Sum-407864.05
Variance0.00073789385
MonotonicityNot monotonic
2023-07-04T04:10:18.776982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29898 8
 
0.2%
-122.35398 7
 
0.2%
-122.32468 6
 
0.2%
-122.33369 6
 
0.2%
-122.32592 5
 
0.1%
-122.32417 5
 
0.1%
-122.33379 5
 
0.1%
-122.33064 5
 
0.1%
-122.31769 5
 
0.1%
-122.3255 4
 
0.1%
Other values (2622) 3278
98.3%
ValueCountFrequency (%)
-122.41425 1
< 0.1%
-122.41182 1
< 0.1%
-122.41178 1
< 0.1%
-122.41169 1
< 0.1%
-122.41037 1
< 0.1%
-122.41036 1
< 0.1%
-122.41031 1
< 0.1%
-122.40976 1
< 0.1%
-122.40974 1
< 0.1%
-122.40901 1
< 0.1%
ValueCountFrequency (%)
-122.2209659 1
< 0.1%
-122.25864 1
< 0.1%
-122.26028 1
< 0.1%
-122.26034 1
< 0.1%
-122.26166 2
0.1%
-122.26172 1
< 0.1%
-122.26177 1
< 0.1%
-122.2618 1
< 0.1%
-122.26216 1
< 0.1%
-122.26223 1
< 0.1%

age
Real number (ℝ)

Distinct113
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.266047
Minimum8
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-07-04T04:10:19.345912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q126
median48
Q374.75
95-th percentile115
Maximum123
Range115
Interquartile range (IQR)48.75

Descriptive statistics

Standard deviation33.014307
Coefficient of variation (CV)0.60837871
Kurtosis-0.86255203
Mean54.266047
Median Absolute Deviation (MAD)24
Skewness0.54463883
Sum180923
Variance1089.9445
MonotonicityNot monotonic
2023-07-04T04:10:19.901881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 71
 
2.1%
9 67
 
2.0%
34 65
 
1.9%
15 65
 
1.9%
35 64
 
1.9%
24 64
 
1.9%
55 63
 
1.9%
22 59
 
1.8%
21 59
 
1.8%
33 59
 
1.8%
Other values (103) 2698
80.9%
ValueCountFrequency (%)
8 35
1.0%
9 67
2.0%
10 50
1.5%
11 35
1.0%
12 15
 
0.4%
13 24
 
0.7%
14 41
1.2%
15 65
1.9%
16 42
1.3%
17 45
1.3%
ValueCountFrequency (%)
123 53
1.6%
122 8
 
0.2%
121 11
 
0.3%
120 3
 
0.1%
119 14
 
0.4%
118 9
 
0.3%
117 18
 
0.5%
116 31
0.9%
115 27
0.8%
114 32
1.0%

Interactions

2023-07-04T04:09:45.041847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:47.464141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:53.498811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:59.219306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:05.410943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:11.270708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:17.154938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:23.222380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:28.998087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:35.086044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:41.298636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:47.242372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:53.298533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:58.800651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:05.256548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:10.425035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:16.519311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:22.016194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:28.355027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:32.957206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:38.465934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:45.413926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:47.820146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:53.708930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:59.646393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:05.658106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:11.553379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:17.429115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:23.541293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:29.321025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:35.385633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:41.604566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:47.742759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:53.598909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:59.105296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:05.562300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:10.733462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:16.741028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:22.384225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:28.636728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:33.216146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:38.796554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:45.691405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:48.140220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:53.921937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:00.084825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:05.921384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:11.850451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:17.726596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:23.843496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:29.605363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:35.666290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:41.885185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:48.031847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:53.860615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:59.562260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:05.855910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:11.014604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:16.942879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:22.864486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:28.907758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:33.424878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:39.107303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:45.979173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:48.463763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:54.121622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:00.386638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:06.225204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:12.160889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:17.979046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:24.078711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:29.872865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:35.977856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:42.206976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:48.317093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:54.119843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:59.889085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:06.180890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:11.492748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:17.160293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:23.232297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:29.124286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:33.699454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:39.430063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:46.277194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:48.717650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:54.327347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:00.677168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:06.514276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:12.456197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:18.285258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:24.348776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:30.143130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:36.287171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:42.502948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:48.611056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:54.400331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:00.175085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:06.563848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-04T04:08:21.001842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:27.076823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:32.920525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:39.222488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:45.361363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:51.402420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:57.045085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:03.343154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:08.724924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:14.929544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:20.011777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:26.335249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:31.488235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:36.436498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:42.740744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:49.305496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:51.749453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:57.213892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:03.741440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:09.444035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:15.464830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:21.280989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:27.315859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:33.212451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:39.508127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:45.598802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:51.650093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:57.279171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:03.637978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:08.922552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:15.177030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:20.195578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:26.630567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:31.690881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:36.726276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:43.175666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:49.480554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:52.056565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:57.514873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:04.003575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:09.742808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:15.735342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:21.567200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:27.540417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:33.504489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:39.800223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:45.813899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:51.946915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:57.531915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:03.925563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:09.118396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:15.361847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:20.375958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:26.922558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:31.907839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:36.976713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:43.574629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:49.694203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:52.379717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:57.811155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:04.308766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:10.035803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:16.051300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:21.855934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:27.857916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:33.801652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:40.096120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:46.031560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:52.258631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:57.819976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:04.181575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:09.326504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:15.613590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:20.755151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:27.187916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:32.121996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:37.277611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:43.861585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:49.896682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:52.673869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:58.118517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:04.589137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:10.338660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:16.348833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:22.161870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:28.163732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:34.118316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:40.399890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:46.329451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:52.464748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:58.047908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:04.489084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:09.538686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:15.805451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:21.105373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:27.446495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:32.321826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:37.587269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:44.145100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:50.119534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:52.972960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:58.403804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:04.862498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:10.601198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:16.633878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:22.599257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:28.445197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:34.378339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:40.684844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:46.631857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:52.701303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:58.305508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:04.776201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:09.794501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:16.036081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:21.382019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:27.763213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:32.523119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:37.870009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:44.391445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:50.441756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:53.264299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:07:58.780240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:05.137396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:11.029272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:16.928408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:22.921918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:28.717303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:34.651592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:41.007518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:46.939910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:53.019934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:08:58.595006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:05.061999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:10.128747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:16.282674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:21.705160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:28.071912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:32.758347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:38.182176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-04T04:09:44.647225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-04T04:10:20.305390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0osebuildingidcouncildistrictcodenumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtutotalghgemissionszipcodelatitudelongitudeagebuildingtypeprimarypropertytypeneighborhoodsteamuseelectricitynaturalgasdefaultdatacompliancestatus
Unnamed: 01.0000.998-0.1470.005-0.010-0.289-0.194-0.278-0.2710.085-0.182-0.181-0.182-0.181-0.274-0.276-0.2270.0950.0990.125-0.1470.2070.2350.1900.2590.0400.1210.1280.000
osebuildingid0.9981.000-0.1460.005-0.012-0.289-0.194-0.278-0.2700.085-0.181-0.180-0.180-0.180-0.272-0.275-0.2260.0960.0990.126-0.1470.2030.2530.1570.2090.0250.1330.0630.000
councildistrictcode-0.147-0.1461.000-0.0390.3350.1550.1530.1450.1270.0740.0920.0780.1090.0990.1460.1380.120-0.1940.512-0.349-0.0010.1490.2520.8800.2140.0310.1420.0980.000
numberofbuildings0.0050.005-0.0391.000-0.0420.102-0.0040.1030.1180.0280.0430.0350.0430.0360.1130.1030.0980.0370.0320.054-0.0460.2380.1530.0480.0810.0000.0000.0000.000
numberoffloors-0.010-0.0120.335-0.0421.0000.4420.2620.4340.4150.1260.022-0.0040.0960.0810.2890.2740.173-0.2300.064-0.114-0.2930.2460.2630.1370.2630.0000.0470.0000.000
propertygfatotal-0.289-0.2890.1550.1020.4421.0000.3460.9830.9300.0820.1850.1580.2090.1850.7570.7410.580-0.092-0.057-0.021-0.3130.1440.1730.0600.1460.0440.0210.0000.150
propertygfaparking-0.194-0.1940.153-0.0040.2620.3461.0000.2220.2720.0140.1980.1770.2460.2290.3050.2920.207-0.1250.015-0.053-0.2390.0520.1560.0610.0840.0000.0160.0000.000
propertygfabuilding_s-0.278-0.2780.1450.1030.4340.9830.2221.0000.9280.0820.1610.1360.1780.1550.7420.7260.576-0.080-0.066-0.016-0.2850.1660.1900.0500.1220.0500.0170.0000.162
largestpropertyusetypegfa-0.271-0.2700.1270.1180.4150.9300.2720.9281.0000.0940.1200.0970.1260.1040.7220.7080.566-0.053-0.049-0.012-0.2910.1480.2260.0500.1430.0550.0180.0000.168
energystarscore0.0850.0850.0740.0280.1260.0820.0140.0820.0941.000-0.447-0.447-0.515-0.524-0.174-0.174-0.099-0.0020.086-0.035-0.0780.1190.1210.0560.0000.0000.1020.1101.000
siteeui_kbtu_sf-0.182-0.1810.0920.0430.0220.1850.1980.1610.120-0.4471.0000.9870.8690.8670.7070.6990.707-0.131-0.0830.0450.0630.1370.2800.0570.1350.0200.1480.0531.000
siteeuiwn_kbtu_sf-0.181-0.1800.0780.035-0.0040.1580.1770.1360.097-0.4470.9871.0000.8390.8610.6850.7020.704-0.126-0.0860.0440.0860.1370.2710.0540.1180.0000.1760.0590.000
sourceeui_kbtu_sf-0.182-0.1800.1090.0430.0960.2090.2460.1780.126-0.5150.8690.8391.0000.9860.6360.6180.463-0.107-0.0520.028-0.0610.1130.2430.0270.0360.0000.0680.0250.000
sourceeuiwn_kbtu_sf-0.181-0.1800.0990.0360.0810.1850.2290.1550.104-0.5240.8670.8610.9861.0000.6180.6260.455-0.107-0.0530.029-0.0400.1140.2460.0220.0340.0000.0690.0270.000
siteenergyuse_kbtu-0.274-0.2720.1460.1130.2890.7570.3050.7420.722-0.1740.7070.6850.6360.6181.0000.9860.873-0.122-0.0950.020-0.1600.1560.2760.0000.1270.0000.0230.0000.000
siteenergyusewn_kbtu-0.276-0.2750.1380.1030.2740.7410.2920.7260.708-0.1740.6990.7020.6180.6260.9861.0000.871-0.118-0.0970.019-0.1480.1390.2980.0410.2100.0000.0480.0000.000
totalghgemissions-0.227-0.2260.1200.0980.1730.5800.2070.5760.566-0.0990.7070.7040.4630.4550.8730.8711.000-0.129-0.1130.025-0.0250.1260.2590.0000.1980.0000.0340.0000.000
zipcode0.0950.096-0.1940.037-0.230-0.092-0.125-0.080-0.053-0.002-0.131-0.126-0.107-0.107-0.122-0.118-0.1291.000-0.0460.008-0.0870.0530.0760.2550.1500.0000.0870.0650.000
latitude0.0990.0990.5120.0320.064-0.0570.015-0.066-0.0490.086-0.083-0.086-0.052-0.053-0.095-0.097-0.113-0.0461.000-0.026-0.1340.1510.2150.5890.3010.0470.1540.1410.000
longitude0.1250.126-0.3490.054-0.114-0.021-0.053-0.016-0.012-0.0350.0450.0440.0280.0290.0200.0190.0250.008-0.0261.0000.0500.1260.1480.4900.1660.0160.0620.1980.000
age-0.147-0.147-0.001-0.046-0.293-0.313-0.239-0.285-0.291-0.0780.0630.086-0.061-0.040-0.160-0.148-0.025-0.087-0.1340.0501.0000.1600.1870.1760.1550.0170.3410.0510.031
buildingtype0.2070.2030.1490.2380.2460.1440.0520.1660.1480.1190.1370.1370.1130.1140.1560.1390.1260.0530.1510.1260.1601.0000.7320.2000.1950.1770.2800.7030.000
primarypropertytype0.2350.2530.2520.1530.2630.1730.1560.1900.2260.1210.2800.2710.2430.2460.2760.2980.2590.0760.2150.1480.1870.7321.0000.2440.2950.1590.3450.6040.000
neighborhood0.1900.1570.8800.0480.1370.0600.0610.0500.0500.0560.0570.0540.0270.0220.0000.0410.0000.2550.5890.4900.1760.2000.2441.0000.2880.0490.1600.1490.000
steamuse0.2590.2090.2140.0810.2630.1460.0840.1220.1430.0000.1350.1180.0360.0340.1270.2100.1980.1500.3010.1660.1550.1950.2950.2881.0000.0000.0120.0170.000
electricity0.0400.0250.0310.0000.0000.0440.0000.0500.0550.0000.0200.0000.0000.0000.0000.0000.0000.0000.0470.0160.0170.1770.1590.0490.0001.0000.0260.0000.474
naturalgas0.1210.1330.1420.0000.0470.0210.0160.0170.0180.1020.1480.1760.0680.0690.0230.0480.0340.0870.1540.0620.3410.2800.3450.1600.0120.0261.0000.0330.008
defaultdata0.1280.0630.0980.0000.0000.0000.0000.0000.0000.1100.0530.0590.0250.0270.0000.0000.0000.0650.1410.1980.0510.7030.6040.1490.0170.0000.0331.0001.000
compliancestatus0.0000.0000.0000.0000.0000.1500.0000.1620.1681.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.4740.0081.0001.000

Missing values

2023-07-04T04:09:50.922994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-04T04:09:52.057920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-04T04:09:52.611514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0osebuildingidbuildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamuseelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeage
001NonResidentialHotel06590000307DOWNTOWN1.01288434088434HotelHotel88434.060.081.69999784.300003182.500000189.0000007226362.57456910.0TrueTrueTrueFalseCompliant249.9898101.047.61220-122.3379996
112NonResidentialHotel06590002207DOWNTOWN1.0111035661506488502Hotel, Parking, RestaurantHotel83880.061.094.80000397.900002176.100006179.3999948387933.08664479.0FalseTrueTrueFalseCompliant295.8698101.047.61317-122.3339327
223NonResidentialHotel06590004757DOWNTOWN1.041956110196718759392HotelHotel756493.043.096.00000097.699997241.899994244.10000672587024.073937112.0TrueTrueTrueFalseCompliant2089.2898101.047.61393-122.3381054
335NonResidentialHotel06590006407DOWNTOWN1.01061320061320HotelHotel61320.056.0110.800003113.300003216.199997224.0000006794584.06946800.5TrueTrueTrueFalseCompliant286.4398101.047.61412-122.3366497
448NonResidentialHotel06590009707DOWNTOWN1.01817558062000113580Hotel, Parking, Swimming PoolHotel123445.075.0114.800003118.699997211.399994215.60000614172606.014656503.0FalseTrueTrueFalseCompliant505.0198121.047.61375-122.3404743
559Nonresidential COSOther06600005607DOWNTOWN1.02972883719860090Police StationPolice Station88830.0NaN136.100006141.600006316.299988320.50000012086616.012581712.0FalseTrueTrueFalseCompliant301.8198101.047.61623-122.3365724
6610NonResidentialHotel06600008257DOWNTOWN1.01183008083008HotelHotel81352.027.070.80000374.500000146.600006154.6999975758795.06062767.5FalseTrueTrueFalseCompliant176.1498101.047.61390-122.3328397
7711NonResidentialOther06600009557DOWNTOWN1.081027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaN61.29999968.800003141.699997152.3000036298131.57067881.5TrueTrueTrueFalseCompliant221.5198101.047.61327-122.3313697
8812NonResidentialHotel09390000807DOWNTOWN1.0151639840163984HotelHotel163984.043.083.69999786.599998180.899994187.19999713723820.014194054.0FalseTrueTrueFalseCompliant392.1698104.047.60294-122.33263119
9913Multifamily MR (5-9)Mid-Rise Multifamily09390001057DOWNTOWN1.0663712149662216Multifamily HousingMultifamily Housing56132.01.081.50000085.599998182.699997187.3999944573777.04807679.5TrueTrueTrueFalseCompliant151.1298104.047.60284-122.33184113
Unnamed: 0osebuildingidbuildingtypeprimarypropertytypetaxparcelidentificationnumbercouncildistrictcodeneighborhoodnumberofbuildingsnumberoffloorspropertygfatotalpropertygfaparkingpropertygfabuilding_slistofallpropertyusetypeslargestpropertyusetypelargestpropertyusetypegfaenergystarscoresiteeui_kbtu_sfsiteeuiwn_kbtu_sfsourceeui_kbtu_sfsourceeuiwn_kbtu_sfsiteenergyuse_kbtusiteenergyusewn_kbtusteamuseelectricitynaturalgasdefaultdatacompliancestatustotalghgemissionszipcodelatitudelongitudeage
3324336650210Nonresidential COSOffice24250391377MAGNOLIA / QUEEN ANNE1.0113661013661OfficeOffice13661.075.036.79999940.900002115.500000128.3999945.026677e+055.585251e+05FalseTrueFalseTrueNaN3.5098119.247.63572-122.3752571
3325336750212Nonresidential COSOther29250490873EAST1.0123445023445Other - RecreationOther - Recreation23445.0NaN254.899994286.500000380.100006413.2000125.976246e+066.716330e+06FalseTrueTrueFalseCompliant259.2298106.047.63228-122.31574111
3326336850219Nonresidential COSMixed Use Property75448002453CENTRAL1.0120050020050Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/ScienceOther - Recreation8108.0NaN90.40000299.400002175.199997184.6000061.813404e+061.993137e+06FalseTrueTrueFalseCompliant60.8198126.447.60775-122.3022529
3327336950220Nonresidential COSOffice41543005852SOUTHEAST1.0115398015398OfficeOffice15398.093.025.20000126.90000064.09999866.6999973.878100e+054.141724e+05FalseTrueTrueTrueNaN7.7998120.647.56440-122.2781363
3328337050221Nonresidential COSOther25240390591DELRIDGE1.0118261018261Other - RecreationOther - Recreation18261.0NaN51.00000056.200001126.000000136.6000069.320821e+051.025432e+06FalseTrueTrueFalseCompliant20.3398126.047.54067-122.3744141
3329337150222Nonresidential COSOffice16240490802GREATER DUWAMISH1.0112294012294OfficeOffice12294.046.069.09999876.699997161.699997176.1000068.497457e+059.430032e+05FalseTrueTrueTrueNaN20.9498126.047.56722-122.3115433
3330337250223Nonresidential COSOther35583000002DOWNTOWN1.0116000016000Other - RecreationOther - Recreation16000.0NaN59.40000265.900002114.199997118.9000029.502762e+051.053706e+06FalseTrueTrueFalseCompliant32.1798113.047.59625-122.3228319
3331337350224Nonresidential COSOther17945011507MAGNOLIA / QUEEN ANNE1.0113157013157Fitness Center/Health Club/Gym, Other - Recreation, Swimming PoolOther - Recreation7583.0NaN438.200012460.100006744.799988767.7999885.765898e+066.053764e+06FalseTrueTrueFalseCompliant223.5498112.047.63644-122.3578449
3332337450225Nonresidential COSMixed Use Property78836031551GREATER DUWAMISH1.0114101014101Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation6601.0NaN51.00000055.500000105.300003110.8000037.194712e+057.828413e+05FalseTrueTrueFalseCompliant22.1198108.047.52832-122.3243134
3333337550226Nonresidential COSMixed Use Property78570020302GREATER DUWAMISH1.0118258018258Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8271.0NaN63.09999870.900002115.800003123.9000021.152896e+061.293722e+06FalseTrueTrueFalseCompliant41.2798118.747.53939-122.2953685